Linear Discriminant Analysis project data on sum of eigenvectors












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I know in Linear Discriminant Analysis (LDA) we project the data on a specific eigenvector or a matrix of eigenvectors to reduce the dimensionality of the data and separate classes. But what happens if we project the data on some vector V that is the sum of a couple of the eigenvectors, or the sum of 1 eigenvector and some random vector? Will that be useful?










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    I know in Linear Discriminant Analysis (LDA) we project the data on a specific eigenvector or a matrix of eigenvectors to reduce the dimensionality of the data and separate classes. But what happens if we project the data on some vector V that is the sum of a couple of the eigenvectors, or the sum of 1 eigenvector and some random vector? Will that be useful?










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      I know in Linear Discriminant Analysis (LDA) we project the data on a specific eigenvector or a matrix of eigenvectors to reduce the dimensionality of the data and separate classes. But what happens if we project the data on some vector V that is the sum of a couple of the eigenvectors, or the sum of 1 eigenvector and some random vector? Will that be useful?










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      I know in Linear Discriminant Analysis (LDA) we project the data on a specific eigenvector or a matrix of eigenvectors to reduce the dimensionality of the data and separate classes. But what happens if we project the data on some vector V that is the sum of a couple of the eigenvectors, or the sum of 1 eigenvector and some random vector? Will that be useful?







      linear-algebra machine-learning dimensional-analysis






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      asked Nov 25 '18 at 22:37









      Sreten Jocić

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